GOALI: Quality Mining - A Novel Framework for Quality Monitoring and Control for Data-rich Manufacturing Systems

GOALI：质量挖掘 - 数据丰富的制造系统质量监控的新框架

• 批准号: 0927323
• 负责人: Jaime Camelio
• 金额: $ 30.45万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0927323&HistoricalAwards=false
• 关键词: GOALI; Quality; Mining; Novel; Framework

This grant provides funding to develop new quality tools for manufacturing systems through incorporating computer and information science principles, such as those used in web mining. These newly envisioned quality methods will make use of data mining techniques such as association, clustering, and significance as a means of handling large amounts of process and product data being collected in current complex manufacturing systems in order to transform the data into usable knowledge. These new quality tools will be developed to store, organize, analyze, model, and visualize large, heterogeneous data sets associated with manufacturing systems for continuous quality and reliability quantification and improvement. These techniques will transform the current data-push quality approach, where reports are often generated without a clear purpose, into a data pull system where the quality system will reflect an awareness of the task environment while reacting to a fault (alarm condition) or a user query. This work includes a strong collaboration with QMC LLC, a data management company who will implement the developed tools, FARO, provider of dimensional data acquisition systems, and Ford Motor Company, who will provide a test bed to validate the proposed tools in an industrial environment. The results of this research will redefine current quality control methods through transforming low-level data into high-content information leading to a deeper understanding of manufacturing and service systems. This will allow for quicker and more accurate failure detections, leading to a significant increase in quality. In addition, the ability to effectively handle data-rich manufacturing systems will stimulate the development and incorporation of advanced sensor and data collection techniques. This will produce a continuous quality improvement attitude based upon heterogeneous data management.

这项拨款提供资金，通过结合计算机和信息科学原理，例如在网络挖掘中使用的原理，为制造系统开发新的高质量工具。这些新设想的质量方法将利用数据挖掘技术，如关联、聚类和重要性，作为处理当前复杂制造系统中收集的大量过程和产品数据的手段，以便将数据转换为可用的知识。这些新的质量工具将用于存储、组织、分析、建模和可视化与制造系统相关的大型异构数据集，以实现持续的质量和可靠性量化和改进。这些技术将把当前的数据推式质量方法（报告通常是在没有明确目的的情况下生成的）转变为数据拉式系统，在这种系统中，质量系统将在对故障（警报条件）或用户查询作出反应时反映对任务环境的认识。这项工作包括与QMC LLC（数据管理公司，负责实施开发的工具）、FARO（维度数据采集系统供应商）和福特汽车公司（福特汽车公司将提供一个测试平台，在工业环境中验证所提出的工具）的强有力合作。本研究的结果将重新定义当前的质量控制方法，通过将低级数据转换为高内容信息，从而更深入地了解制造和服务系统。这将允许更快、更准确的故障检测，从而显著提高质量。此外，有效处理数据丰富的制造系统的能力将刺激先进传感器和数据收集技术的发展和结合。这将产生基于异构数据管理的持续质量改进态度。